Filter
Reset all

Subjects

Content Types

Countries

API

Data access

Data access restrictions

Database access

Database access restrictions

Database licenses

Data licenses

Data upload

Data upload restrictions

Enhanced publication

Institution responsibility type

Institution type

Keywords

Metadata standards

PID systems

Provider types

Quality management

Repository languages

Software

Syndications

Repository types

Versioning

  • * at the end of a keyword allows wildcard searches
  • " quotes can be used for searching phrases
  • + represents an AND search (default)
  • | represents an OR search
  • - represents a NOT operation
  • ( and ) implies priority
  • ~N after a word specifies the desired edit distance (fuzziness)
  • ~N after a phrase specifies the desired slop amount
  • 1 (current)
Found 14 result(s)
The CONP portal is a web interface for the Canadian Open Neuroscience Platform (CONP) to facilitate open science in the neuroscience community. CONP simplifies global researcher access and sharing of datasets and tools. The portal internalizes the cycle of a typical research project: starting with data acquisition, followed by processing using already existing/published tools, and ultimately publication of the obtained results including a link to the original dataset. From more information on CONP, please visit https://conp.ca
ChemSpider is a free chemical structure database providing fast access to over 58 million structures, properties and associated information. By integrating and linking compounds from more than 400 data sources, ChemSpider enables researchers to discover the most comprehensive view of freely available chemical data from a single online search. It is owned by the Royal Society of Chemistry. ChemSpider builds on the collected sources by adding additional properties, related information and links back to original data sources. ChemSpider offers text and structure searching to find compounds of interest and provides unique services to improve this data by curation and annotation and to integrate it with users’ applications.
The main goal of the ECCAD project is to provide scientific and policy users with datasets of surface emissions of atmospheric compounds, and ancillary data, i.e. data required to estimate or quantify surface emissions. The supply of ancillary data - such as maps of population density, maps of fires spots, burnt areas, land cover - could help improve and encourage the development of new emissions datasets. ECCAD offers: Access to global and regional emission inventories and ancillary data, in a standardized format Quick visualization of emission and ancillary data Rationalization of the use of input data in algorithms or emission models Analysis and comparison of emissions datasets and ancillary data Tools for the evaluation of emissions and ancillary data ECCAD is a dynamical and interactive database, providing the most up to date datasets including data used within ongoing projects. Users are welcome to add their own datasets, or have their regional masks included in order to use ECCAD tools.
GeoCommons is the public community of GeoIQ users who are building an open repository of data and maps for the world. The GeoIQ platform includes a large number of features that empower you to easily access, visualize and analyze your data. The GeoIQ platform powers the growing GeoCommons community of over 25,000 members actively creating and sharing hundreds of thousands of datasets and maps across the world. With GeoCommons, anyone can contribute and share open data, easily build shareable maps and collaborate with others.
The National Deep Submergence Facility (NDSF) operates the Human Occupied Vehicle (HOV) Alvin, the Remote Operated Vehicle (ROV) Jason 2, and the Autonomous Underwater Vehicle (AUV) Sentry. Data acquired with these platforms is provided both to the science party on each expedition, and to the Woods Hole Oceanographic Institution (WHOI) Data Library.
---<<< This repository is no longer available. This record is out-dated >>>--- The ONS challenge contains open solubility data, experiments with raw data from different scientists and institutions. It is part of the The Open Notebook Science wiki community, ideally suited for community-wide collaborative research projects involving mathematical modeling and computer simulation work, as it allows researchers to document model development in a step-by-step fashion, then link model prediction to experiments that test the model, and in turn, use feeback from experiments to evolve the model. By making our laboratory notebooks public, the evolutionary process of a model can be followed in its totality by the interested reader. Researchers from laboratories around the world can now follow the progress of our research day-to-day, borrow models at various stages of development, comment or advice on model developments, discuss experiments, ask questions, provide feedback, or otherwise contribute to the progress of science in any manner possible.
The Polar Rock Repository (PRR) at the Byrd Polar and Climate Research Center (BPCRC) at Ohio State University is an NSF-OPP funded facility that provides access to rock, terrestrial drill core, glacial deposits and marine dredge samples from Antarctica and the Southern Ocean. The polar rock collection and database includes field notes, photos, maps, cores, powder and mineral residues, thin sections, and residues. Rock samples may be borrowed for research by university scientists. Samples may also be borrowed for educational or museum use in the United States.
State of the Salmon provides data on abundance, diversity, and ecosystem health of wild salmon populations specific to the Pacific Ocean, North Western North America, and Asia. Data downloads are available using two geographic frameworks: Salmon Ecoregions or Hydro 1K.
Country
The Global Proteome Machine (GPM) is a protein identification database. This data repository allows users to post and compare results. GPM's data is provided by contributors like The Informatics Factory, University of Michigan, and Pacific Northwestern National Laboratories. The GPM searchable databases are: GPMDB, pSYT, SNAP, MRM, PEPTIDE and HOT.
This database will provide a central location for scientists to browse uniquely observed proteoforms and to contribute their own datasets. Top-down proteomics is a method of protein identification that uses an ion trapping mass spectrometer to store an isolated protein ion for mass measurement and tandem mass spectrometry analysis.
Global Ocean Ecosystem Dynamics (GLOBEC) is the International Geosphere-Biosphere Programme (IGBP) core project responsible for understanding how global change will affect the abundance, diversity and productivity of marine populations. The programme was initiated by SCOR and the IOC of UNESCO in 1991, to understand how global change will affect the abundance, diversity and productivity of marine populations comprising a major component of oceanic ecosystems. The aim of GLOBEC is to advance our understanding of the structure and functioning of the global ocean ecosystem, its major subsystems, and its response to physical forcing so that a capability can be developed to forecast the responses of the marine ecosystem to global change. U.S. GLOBEC Programm includes the Georges Bank / NW Atlantic Programm, the Northeast Pacific Programm and the Southern Ocean Program.
The United Nations Data (UND) site provides access to 32 databases and over 60million records. UN Statistical Databases include datasets on Energy Statistics, International Finances, The State of the World’s Children, and World Contraceptive Use; among many other global social, environmental and economic subjects.
VertNet is a NSF-funded collaborative project that makes biodiversity data free and available on the web. VertNet is a tool designed to help people discover, capture, and publish biodiversity data. It is also the core of a collaboration between hundreds of biocollections that contribute biodiversity data and work together to improve it. VertNet is an engine for training current and future professionals to use and build upon best practices in data quality, curation, research, and data publishing. Yet, VertNet is still the aggregate of all of the information that it mobilizes. To us, VertNet is all of these things and more.
WorldData.AI comes with a built-in workspace – the next-generation hyper-computing platform powered by a library of 3.3 billion curated external trends. WorldData.AI allows you to save your models in its “My Models Trained” section. You can make your models public and share them on social media with interesting images, model features, summary statistics, and feature comparisons. Empower others to leverage your models. For example, if you have discovered a previously unknown impact of interest rates on new-housing demand, you may want to share it through “My Models Trained.” Upload your data and combine it with external trends to build, train, and deploy predictive models with one click! WorldData.AI inspects your raw data, applies feature processors, chooses the best set of algorithms, trains and tunes multiple models, and then ranks model performance.